OpenRaiser/Intern-Atlas huggingface dataset:
OMEGA: Optimized Multimodal Position Encoding Index Derivation with Global Adaptive Scaling for Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language Models (VLMs) have demonstrated strong performance across various multimodal tasks, where position encoding plays a vital role in modeling both the sequential structure of textual information and the spatial structure of visual information. However, current VLMs commonly adopt modality-unified 1D or 2D positional indexing strategies, which treat textual and visual tokens uniformly without accounting for their distinct structural properties and sequential continuity for text and spatial coherence for vision. To address this limitation, we propose OMEGA, a novel position encoding framework that employs Modality-Specific Position Encoding (MSPE) to assign positional indices while preserving the inherent structures of each modality across separate coordinate dimensions. Additionally, to align the information density of multimodal data in the positional index space, OMEGA introduces Global Adaptive Encoding Step Scaling (GAESS), which adaptively adjusts the position encoding step size of visual tokens based on the embedding entropy of both modalities. Experimental results demonstrate that OMEGA consistently enhances VLM performance across diverse architectures and VQA benchmarks. On visual-intensive tasks, OMEGA achieves up to 3.43% improvement over baseline position encoding strategies on Qwen2.5-VL-3B, with consistent gains observed across larger models including Qwen2.5-VL-7B and LLaVA-v1.5-7B.
Efficient Query Repair for Aggregate Constraints
Venue: arXiv cs.DB 2025
Year: 2025

In many real-world scenarios, query results must satisfy domain-specific constraints. For instance, a minimum percentage of interview candidates selected based on their qualifications should be female. These requirements can be expressed as constraints over an arithmetic combination of aggregates evaluated on the result of the query. In this work, we study how to repair a query to fulfill such constraints by modifying the filter predicates of the query. We introduce a novel query repair technique that leverages bounds on sets of candidate solutions and interval arithmetic to efficiently prune the search space. We demonstrate experimentally, that our technique significantly outperforms baselines that consider a single candidate at a time.
Identifying Slug Formation in Oil Well Pipelines: A Use Case from Industrial Analytics
Venue: arXiv cs.LG 2025
Year: 2025

Slug formation in oil and gas pipelines poses significant challenges to operational safety and efficiency, yet existing detection approaches are often offline, require domain expertise, and lack real-time interpretability. We present an interactive application that enables end-to-end data-driven slug detection through a compact and user-friendly interface. The system integrates data exploration and labeling, configurable model training and evaluation with multiple classifiers, visualization of classification results with time-series overlays, and a real-time inference module that generates persistence-based alerts when slug events are detected. The demo supports seamless workflows from labeled CSV uploads to live inference on unseen datasets, making it lightweight, portable, and easily deployable. By combining domain-relevant analytics with novel UI/UX features such as snapshot persistence, visual labeling, and real-time alerting, our tool adds significant dissemination value as both a research prototype and a practical industrial application. The demo showcases how interactive human-in-the-loop ML systems can bridge the gap between data science methods and real-world decision-making in critical process industries, with broader applicability to time-series fault diagnosis tasks beyond oil and gas.
Training with Fewer Bits: Unlocking Edge LLMs Training with Stochastic Rounding
Venue: arXiv cs.LG 2025
Year: 2025

LLM training is resource-intensive. Quantized training improves computational and memory efficiency but introduces quantization noise, which can hinder convergence and degrade model accuracy. Stochastic Rounding (SR) has emerged as a theoretically attractive alternative to deterministic rounding, offering unbiased gradient estimates. However, its interaction with other training factors -- especially batch size -- remains under explored. In this paper, we present a theoretical and empirical study of mini-batch stochastic gradient descent (SGD) with SR, showing that increased batch sizes can compensate for reduced precision during back-propagation. Furthermore, we show that quantizing weights and activations impacts gradient variance in distinct ways. Our experiments validate these theoretical insights.
A Meta-Cognitive Swarm Intelligence Framework for Resilient UAV Navigation in GPS-Denied and Cluttered Environments
Venue: arXiv cs.CE 2025
Year: 2025

Autonomous navigation of UAV swarms in perceptually-degraded environments, where GPS is unavailable and terrain is densely cluttered, presents a critical bottleneck for real-world deployment. Existing optimization-based planners lack the resilience to avoid catastrophic convergence to local optima under such uncertainty. Inspired by principles of computational meta-cognition, this paper introduces a novel swarm intelligence framework that enables a fleet of UAVs to autonomously sense, adapt, and recover from planning failures in real-time. At its core is the Self-Learning Slime Mould Algorithm (SLSMA), which integrates three meta-cognitive layers: a situation-aware search strategy that dynamically selects between exploration and exploitation based on perceived search stagnation; a collective memory mechanism that allows the swarm to learn from and avoid previously failed trajectories; and an adaptive recovery behavior that triggers global re-exploration upon entrapment. We formulate the multi-UAV trajectory problem as a resilient planning challenge, with a cost function that penalizes not only path length and collisions but also navigational uncertainty and proximity to failure states. Extensive simulations in synthetically complex 3D worlds and against the CEC 2017 benchmark suite demonstrate the framework's superior performance. The SLSMA does not merely optimize paths; it generates resilient trajectories, demonstrating a 99.5% mission success rate and significantly outperforming state-of-the-art metaheuristics in recovery speed and solution reliability. This work provides a foundational step towards truly autonomous swarms capable of persistent operation in denied and dynamic environments.
A Simple Logic of Cohesive Group Agency
Venue: arXiv cs.LO 2025
Year: 2025

We propose a structure to represent the social fabric of a group. We call it the `cohesion network' of the group. It can be seen as a graph whose vertices are strict subgroups and whose edges indicate a prescribed `pro-social behaviour' from one subgroup towards another. In social psychology, pro-social behaviours are building blocks of full-blown cooperation, which we assimilate here with `group cohesiveness'. We then define a formal framework to study cohesive group agency. To do so, we simply instantiate pro-social behaviour with the more specific relation of `successful assistance' between acting entities in a group. The relations of assistance within a group at the moment of agency constitute the social fabric of the cohesive group agency. We build our logical theory upon the logic of agency "bringing-it-about". We obtain a family of logics of cohesive group agency, one for every class of cohesion networks.
Sustainability of Machine Learning-Enabled Systems: The Machine Learning Practitioner's Perspective
Venue: arXiv cs.SE 2025
Year: 2025

Software sustainability is a key multifaceted non-functional requirement that encompasses environmental, social, and economic concerns, yet its integration into the development of Machine Learning (ML)-enabled systems remains an open challenge. While previous research has explored high-level sustainability principles and policy recommendations, limited empirical evidence exists on how sustainability is practically managed in ML workflows. Existing studies predominantly focus on environmental sustainability, e.g., carbon footprint reduction, while missing the broader spectrum of sustainability dimensions and the challenges practitioners face in real-world settings. To address this gap, we conduct an empirical study to characterize sustainability in ML-enabled systems from a practitioner's perspective. We investigate (1) how ML engineers perceive and describe sustainability, (2) the software engineering practices they adopt to support it, and (3) the key challenges hindering its adoption. We first perform a qualitative analysis based on interviews with eight experienced ML engineers, followed by a large-scale quantitative survey with 203 ML practitioners. Our key findings reveal a significant disconnection between sustainability awareness and its systematic implementation, highlighting the need for more structured guidelines, measurement frameworks, and regulatory support.
Random Spiking Neural Networks are Stable and Spectrally Simple
Venue: arXiv cs.LG 2025
Year: 2025

Spiking neural networks (SNNs) are a promising paradigm for energy-efficient computation, yet their theoretical foundations-especially regarding stability and robustness-remain limited compared to artificial neural networks. In this work, we study discrete-time leaky integrate-and-fire (LIF) SNNs through the lens of Boolean function analysis. We focus on noise sensitivity and stability in classification tasks, quantifying how input perturbations affect outputs. Our main result shows that wide LIF-SNN classifiers are stable on average, a property explained by the concentration of their Fourier spectrum on low-frequency components. Motivated by this, we introduce the notion of spectral simplicity, which formalizes simplicity in terms of Fourier spectrum concentration and connects our analysis to the simplicity bias observed in deep networks. Within this framework, we show that random LIF-SNNs are biased toward simple functions. Experiments on trained networks confirm that these stability properties persist in practice. Together, these results provide new insights into the stability and robustness properties of SNNs.
DPMon: a Differentially-Private Query Engine for Passive Measurements
Venue: arXiv cs.NI 2025
Year: 2025

Passive monitoring is a network measurement technique which analyzes the traffic carried by an operational network. It has several applications for traffic engineering, Quality of Experience monitoring and cyber security. However, it entails the processing of personal information, thus, threatening users' privacy. In this work, we propose DPMon, a tool to run privacy-preserving queries to a dataset of passive network measurements. It exploits differential privacy to perturb the output of the query to preserve users' privacy. DPMon can exploit big data infrastructures running Apache Spark and operate on different data formats. We show that DPMon allows extracting meaningful insights from the data, while at the same time controlling the amount of disclosed information.
The Biased Oracle: Assessing LLMs' Understandability and Empathy in Medical Diagnoses
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) show promise for supporting clinicians in diagnostic communication by generating explanations and guidance for patients. Yet their ability to produce outputs that are both understandable and empathetic remains uncertain. We evaluate two leading LLMs on medical diagnostic scenarios, assessing understandability using readability metrics as a proxy and empathy through LLM-as-a-Judge ratings compared to human evaluations. The results indicate that LLMs adapt explanations to socio-demographic variables and patient conditions. However, they also generate overly complex content and display biased affective empathy, leading to uneven accessibility and support. These patterns underscore the need for systematic calibration to ensure equitable patient communication. The code and data are released: https://github.com/Jeffateth/Biased_Oracle
LLMs Position Themselves as More Rational Than Humans: Emergence of AI Self-Awareness Measured Through Game Theory
Venue: arXiv cs.AI 2025
Year: 2025

As Large Language Models (LLMs) grow in capability, do they develop self-awareness as an emergent behavior? And if so, can we measure it? We introduce the AI Self-Awareness Index (AISAI), a game-theoretic framework for measuring self-awareness through strategic differentiation. Using the "Guess 2/3 of Average" game, we test 28 models (OpenAI, Anthropic, Google) across 4,200 trials with three opponent framings: (A) against humans, (B) against other AI models, and (C) against AI models like you. We operationalize self-awareness as the capacity to differentiate strategic reasoning based on opponent type. Finding 1: Self-awareness emerges with model advancement. The majority of advanced models (21/28, 75%) demonstrate clear self-awareness, while older/smaller models show no differentiation. Finding 2: Self-aware models rank themselves as most rational. Among the 21 models with self-awareness, a consistent rationality hierarchy emerges: Self > Other AIs > Humans, with large AI attribution effects and moderate self-preferencing. These findings reveal that self-awareness is an emergent capability of advanced LLMs, and that self-aware models systematically perceive themselves as more rational than humans. This has implications for AI alignment, human-AI collaboration, and understanding AI beliefs about human capabilities.
Fast-SmartWay: Panoramic-Free End-to-End Zero-Shot Vision-and-Language Navigation
Venue: arXiv cs.RO 2025
Year: 2025

Recent advances in Vision-and-Language Navigation in Continuous Environments (VLN-CE) have leveraged multimodal large language models (MLLMs) to achieve zero-shot navigation. However, existing methods often rely on panoramic observations and two-stage pipelines involving waypoint predictors, which introduce significant latency and limit real-world applicability. In this work, we propose Fast-SmartWay, an end-to-end zero-shot VLN-CE framework that eliminates the need for panoramic views and waypoint predictors. Our approach uses only three frontal RGB-D images combined with natural language instructions, enabling MLLMs to directly predict actions. To enhance decision robustness, we introduce an Uncertainty-Aware Reasoning module that integrates (i) a Disambiguation Module for avoiding local optima, and (ii) a Future-Past Bidirectional Reasoning mechanism for globally coherent planning. Experiments on both simulated and real-robot environments demonstrate that our method significantly reduces per-step latency while achieving competitive or superior performance compared to panoramic-view baselines. These results demonstrate the practicality and effectiveness of Fast-SmartWay for real-world zero-shot embodied navigation.
Using Synthetic Data to estimate the True Error is theoretically and practically doable
Venue: arXiv cs.LG 2025
Year: 2025

Accurately evaluating model performance is crucial for deploying machine learning systems in real-world applications. Traditional methods often require a sufficiently large labeled test set to ensure a reliable evaluation. However, in many contexts, a large labeled dataset is costly and labor-intensive. Therefore, we sometimes have to do evaluation by a few labeled samples, which is theoretically challenging. Recent advances in generative models offer a promising alternative by enabling the synthesis of high-quality data. In this work, we make a systematic investigation about the use of synthetic data to estimate the test error of a trained model under limited labeled data conditions. To this end, we develop novel generalization bounds that take synthetic data into account. Those bounds suggest novel ways to optimize synthetic samples for evaluation and theoretically reveal the significant role of the generator's quality. Inspired by those bounds, we propose a theoretically grounded method to generate optimized synthetic data for model evaluation. Experimental results on simulation and tabular datasets demonstrate that, compared to existing baselines, our method achieves accurate and more reliable estimates of the test error.
PathFinder: Efficiently Supporting Conjunctions and Disjunctions for Filtered Approximate Nearest Neighbor Search
Venue: arXiv cs.DB 2025
Year: 2025

Filtered approximate nearest neighbor search (ANNS) restricts the search to data objects whose attributes satisfy a given filter and retrieves the top-$K$ objects that are most semantically similar to the query object. Many graph-based ANNS indexes are proposed to enable efficient filtered ANNS but remain limited in applicability or performance: indexes optimized for a specific attribute achieve high efficiency for filters on that attribute but fail to support complex filters with arbitrary conjunctions and disjunctions over multiple attributes. Inspired by the design of relational databases, this paper presents PathFinder, a new indexing framework that allows users to selectively create ANNS indexes optimized for filters on specific attributes and employs a cost-based optimizer to efficiently utilize them for processing complex filters. PathFinder includes three novel techniques: 1) a new optimization metric that captures the tradeoff between query execution time and accuracy, 2) a two-phase optimization for handling filters with conjunctions and disjunctions, and 3) an index borrowing optimization that uses an attribute-specific index to process filters on another attribute. Experiments on four real-world datasets show that PathFinder outperforms the best baseline by up to 9.8x in query throughput at recall 0.95.
Integrating Visual and X-Ray Machine Learning Features in the Study of Paintings by Goya
Venue: arXiv cs.CV 2025
Year: 2025

Art authentication of Francisco Goya's works presents complex computational challenges due to his heterogeneous stylistic evolution and extensive historical patterns of forgery. We introduce a novel multimodal machine learning framework that applies identical feature extraction techniques to both visual and X-ray radiographic images of Goya paintings. The unified feature extraction pipeline incorporates Grey-Level Co-occurrence Matrix descriptors, Local Binary Patterns, entropy measures, energy calculations, and colour distribution analysis applied consistently across both imaging modalities. The extracted features from both visual and X-ray images are processed through an optimised One-Class Support Vector Machine with hyperparameter tuning. Using a dataset of 24 authenticated Goya paintings with corresponding X-ray images, split into an 80/20 train-test configuration with 10-fold cross-validation, the framework achieves 97.8% classification accuracy with a 0.022 false positive rate. Case study analysis of ``Un Gigante'' demonstrates the practical efficacy of our pipeline, achieving 92.3% authentication confidence through unified multimodal feature analysis. Our results indicate substantial performance improvement over single-modal approaches, establishing the effectiveness of applying identical computational methods to both visual and radiographic imagery in art authentication applications.
OceanAI: A Conversational Platform for Accurate, Transparent, Near-Real-Time Oceanographic Insights
Venue: arXiv cs.CL 2025
Year: 2025

Artificial intelligence is transforming the sciences, yet general conversational AI systems often generate unverified "hallucinations" undermining scientific rigor. We present OceanAI, a conversational platform that integrates the natural-language fluency of open-source large language models (LLMs) with real-time, parameterized access to authoritative oceanographic data streams hosted by the National Oceanic and Atmospheric Administration (NOAA). Each query such as "What was Boston Harbor's highest water level in 2024?" triggers real-time API calls that identify, parse, and synthesize relevant datasets into reproducible natural-language responses and data visualizations. In a blind comparison with three widely used AI chat-interface products, only OceanAI produced NOAA-sourced values with original data references; others either declined to answer or provided unsupported results. Designed for extensibility, OceanAI connects to multiple NOAA data products and variables, supporting applications in marine hazard forecasting, ecosystem assessment, and water-quality monitoring. By grounding outputs and verifiable observations, OceanAI advances transparency, reproducibility, and trust, offering a scalable framework for AI-enabled decision support within the oceans. A public demonstration is available at https://oceanai.ai4ocean.xyz.
FastBoost: Progressive Attention with Dynamic Scaling for Efficient Deep Learning
Venue: arXiv cs.CV 2025
Year: 2025

We present FastBoost, a parameter-efficient neural architecture that achieves state-of-the-art performance on CIFAR benchmarks through a novel Dynamically Scaled Progressive Attention (DSPA) mechanism. Our design establishes new efficiency frontiers with: CIFAR-10: 95.57% accuracy (0.85M parameters) and 93.80% (0.37M parameters) CIFAR-100: 81.37% accuracy (0.92M parameters) and 74.85% (0.44M parameters) The breakthrough stems from three fundamental innovations in DSPA: (1) Adaptive Fusion: Learnt channel-spatial attention blending with dynamic weights. (2) Phase Scaling: Training-stage-aware intensity modulation (from 0.5 to 1.0). (3) Residual Adaptation: Self-optimized skip connections (gamma from 0.5 to 0.72). By integrating DSPA with enhanced MBConv blocks, FastBoost achieves a 2.1 times parameter reduction over MobileNetV3 while improving accuracy by +3.2 percentage points on CIFAR-10. The architecture features dual attention pathways with real-time weight adjustment, cascaded refinement layers (increasing gradient flow by 12.7%), and a hardware-friendly design (0.28G FLOPs). This co-optimization of dynamic attention and efficient convolution operations demonstrates unprecedented parameter-accuracy trade-offs, enabling deployment in resource-constrained edge devices without accuracy degradation.
DPO-F+: Aligning Code Repair Feedback with Developers' Preferences
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) are increasingly applied to software engineering tasks, especially code repair. However, developers often struggle to interpret model outputs, limiting effective human-AI teaming. Prior work largely optimizes repaired code while under-addressing the natural-language feedback that enables comprehension and iterative improvement. We present DPO-f+, a novel framework that aligns code-repair feedback with developer needs and profiles. It (1) formalizes developer-profiled, domain-specific metrics for feedback alignment; (2) automatically constructs pairwise preference datasets from code-repair tasks; (3) fine-tunes using Direct Preference Optimization (DPO) augmented with a lightweight margin signal; and (4) provides an automated feedback evaluation protocol. Empirically, DPO-f+ outperforms both the baseline and standard DPO on generated-code accuracy and overall feedback alignment. On novice programming tasks, DPO-f+ raises the top-1 pass rate by 5.71 percentage points (pp) over the baseline and by 3.30 pp over DPO. On the more challenging SWE-bench Lite benchmark, it increases the issue-resolution rate by 1.67 pp over DPO and by 4.67 pp over the baseline. It also achieves the largest improvement in feedback alignment, outperforming DPO and the baseline. By aligning feedback more closely with developer needs, DPO-f+ turns LLM-assisted repair from one-shot outputs into a collaborative sensemaking workflow, providing a practical approach to enhancing code comprehension and fostering more effective human-AI teaming in software engineering.